4.56 out of 5
4.56
16 reviews on Udemy

Statistics for Data Science using Python

Data Science
Instructor:
Shan Singh
742 students enrolled
Understand the fundamentals of statistics
Understand the Stats concepts needed for data science using Python
Distinguish and work with different types of distributions
Calculate the measures of central tendency, asymmetry, and Skewness in Data
Under-stand Hypothesis Testing & its use-cases too
Get hands-on stats
if you do have a math background, you’ll definitely enjoy this fun, hands-on method too.

If You want to be a Data Scientist or Data Analyst then brushing up on your statistics skills is something you need to do.

But it’s just hard to get started with Data Science in most of the course you will find theoritical knowledge on stats not having practical knowledge

I have explained Each topic in a easiest way as well as its implementation in Python from Scratch (most demanding language of Data Science Industry)

That’s exactly why I have created this course for you!

Here you will quickly get the  essential stats knowledge for a Data Scientist or Analyst.

I have included real-world use-cases of business challenges to show you how you could apply Stats knowledge to boost your career.

At the same time you can master topics such as Descriptive Stats, distributions, z-test, the Central Limit Theorem, hypothesis testing,  & many more!

So what are you waiting for?

Enroll now and & get a transition into Data Science

Intro to Stats

1
Why to Learn Statistics?
2
What Is Statistics?
3
Use of Statistics in Data Science

Intro to Python for Statistics

1
What is Python & need of Python in Data Science!
2
How Python works
3
Installation of Anaconda Navigator

Basics of Python (Python Module 1)

1
Variables in python & its use
2
Rules for Variable-Declaration in Python
3
What are Keywords in Python?
4
Data types In Python
5
Operators In Python
6
Indentation in Python
7
Conditional Statements in Python
8
Loops in Python (For loop)

Python Module 2 (Data Structures in Python)

1
List & various operations on list
2
List in Python
3
Set & its use-cases
4
Set in Python
5
Dictionary & its applications
6
Dictionary in Python

Python module 3

1
Intro to Pandas
2
Intro to Numpy
3
Intro to Seaborn

Statistics Module 1

1
Random Variable, Population and Sample Statistics
2
Types of Statistics
3
What are Outliers & Measures of Central Tendancy(Mean,Median,Mode) ?
4
Mean,Mode Median implementation using Python
5
Measures of Spread (Variance,Standard Dev. ,Range,Inter-Quantile Range)
6
Outliers Detection and Removal using Python
7
Skewness in Data

Statistics Module 2

1
Frequency Tables & Histogram
2
Frequency Tables & Histogram in Python
3
Types of Analysis
4
Types of Analysis in Python
5
Covariance and Co-relation
6
Co-relation using Python

Statistics Module 3

1
Intro to Probability
2
What is Prob. Density Function(PDF) & Cumalative Demnsity Function(CDF) ?
3
Bayes Theorem
4
What is a Distribution and why we use it?
5
Bionomial Distribution
6
Binomial Dist. in Python
7
Poisson's Distribution
8
Poisson Distribution in Python
9
Normal Distribution
10
Normal Distribution in Python
11
Implementation of Z-score(Standarization) in python
12
Log-Normal Distribution and Heavy-Tailed Distribution
13
Log-Normal and Heavy Tailed Dist.. in Python
14
Q-Q Plot
15
Central Limit Theorem
16
Central Limit Theorem implemntation in Python
17
Chebyshew's Inequality
18
Estimation Problem based on Z-stats

Statistics Module 4

1
Hypothesis testing
2
1-tailed and 2-Tailed Test
3
Critical_Region
4
Hypothesis Table
5
Level of Significance
6
P-value
7
T-test and various types of T-test
8
T-test in Python
9
Chi-Square Test
10
Anova Test
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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